We directly apply the benchmark protocol (including data preparation) as proposed in  [1]}.
In this experiment, we perform similarity search, which requires a set of Queries and a Database.
For Queries, we use the mini test split of Casual Conversations  [2]} which has \(2,982\)  videos (two videos per participant with one dark and one bright lighting video when possible).
The dataset provides self-identified age (from 18 to 85) and gender (`male', `female', `other' and `n/a') labels along with annotated Fitzpatrick skintone [3]}.
For each video, when possible, we use the middle frame and use the face crops from each image.
As Database, we use the UTK-Faces  [4]} dataset which has \(24,108\)  face images annotated with apparent age and gender labels.
Following Buolamwini  [5]}, we group the Fitzpatrick scale into two types: Lighter (Type I to Type III) and Darker (Type IV to Type VI).
As a result, we obtain four gender-skintone subgroups [female, male] \(\times \)  [lighter, darker] and four age subgroups \(18-30\) , \(30-45\) , \(45-70\) , \(70+\) .
We extract features on Casual Conversations and UTK-Faces and for each query, retrieve the closest image in the Database based on cosine similarity metric.
We perform similarity search for the gender attribute and measure P@1 for different sub-groups: gender, skintone and age groups.
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